






"""simple docstring"""
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor



class        UpperCamelCase__ :







    """simple docstring"""







    def __init__(		self :	Any				,    UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	Union[str, Any]=1_3				,    UpperCamelCase_ :	int=3				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	int=True				,    UpperCamelCase_ :	Any=0.1				,    UpperCamelCase_ :	Any=0.1				,    UpperCamelCase_ :	List[str]=2_2_4				,    UpperCamelCase_ :	Any=1_0_0_0				,    UpperCamelCase_ :	Union[str, Any]=[3, 3, 6, 4]				,    UpperCamelCase_ :	Optional[int]=[4_8, 5_6, 1_1_2, 2_2_0]				,    ):





           '''simple docstring'''

           __magic_name__      				=    parent
           __magic_name__      				=    batch_size
           __magic_name__      				=    num_channels
           __magic_name__      				=    is_training
           __magic_name__      				=    use_labels
           __magic_name__      				=    hidden_dropout_prob
           __magic_name__      				=    attention_probs_dropout_prob
           __magic_name__      				=    num_labels
           __magic_name__      				=    image_size
           __magic_name__      				=    layer_depths
           __magic_name__      				=    embed_dims







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__      				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

           __magic_name__      				=    None
           if self.use_labels:
                  __magic_name__      				=    ids_tensor([self.batch_size]				,    self.num_labels )

           __magic_name__      				=    self.get_config()

           return config, pixel_values, labels







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           return SwiftFormerConfig(
               depths=self.layer_depths				,    embed_dims=self.embed_dims				,    mlp_ratio=4				,    downsamples=[True, True, True, True]				,    hidden_act='gelu'				,    num_labels=self.num_labels				,    down_patch_size=3				,    down_stride=2				,    down_pad=1				,    drop_rate=0.0				,    drop_path_rate=0.0				,    use_layer_scale=UpperCamelCase_				,    layer_scale_init_value=1e-5				,    )







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    SwiftFormerModel(config=UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_ )
           self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.embed_dims[-1], 7, 7) )







    def 				a__			(		self :	int				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Tuple				,    UpperCamelCase_ :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.num_labels
           __magic_name__      				=    SwiftFormerForImageClassification(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()
           __magic_name__      				=    model(UpperCamelCase_				,    labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels) )

           __magic_name__      				=    SwiftFormerForImageClassification(UpperCamelCase_ )
           model.to(UpperCamelCase_ )
           model.eval()

           __magic_name__      				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
           __magic_name__      				=    model(UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels) )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           ((__magic_name__)				,  (__magic_name__)				,  (__magic_name__))      				=    self.prepare_config_and_inputs()
           __magic_name__      				=    {'pixel_values': pixel_values}
           return config, inputs_dict



@require_torch
class        UpperCamelCase__ (				a_							,	a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __UpperCAmelCase           = (
        {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False
    __UpperCAmelCase           = False







    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    SwiftFormerModelTester(self )
           __magic_name__      				=    ConfigTester(
               self				,    config_class=UpperCamelCase_				,    has_text_modality=UpperCamelCase_				,    hidden_size=3_7				,    num_attention_heads=1_2				,    num_hidden_layers=1_2				,    )







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.config_tester.run_common_tests()







    @unittest.skip(reason='SwiftFormer does not use inputs_embeds' )
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           pass







    def 				a__			(		self :	Dict ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  __magic_name__      				=    model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(UpperCamelCase_				,    nn.Linear ) )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  __magic_name__      				=    inspect.signature(model.forward )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  __magic_name__      				=    [*signature.parameters.keys()]

                  __magic_name__      				=    ['pixel_values']
                  self.assertListEqual(arg_names[:1]				,    UpperCamelCase_ )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_ )







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )







    @slow
    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __magic_name__      				=    SwiftFormerModel.from_pretrained(UpperCamelCase_ )
                  self.assertIsNotNone(UpperCamelCase_ )







    @unittest.skip(reason='SwiftFormer does not output attentions' )
    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           pass







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           def check_hidden_states_output(UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	Union[str, Any] ):
                  __magic_name__      				=    model_class(UpperCamelCase_ )
                  model.to(UpperCamelCase_ )
                  model.eval()

                  with torch.no_grad():
                         __magic_name__      				=    model(**self._prepare_for_class(UpperCamelCase_				,    UpperCamelCase_ ) )

                  __magic_name__      				=    outputs.hidden_states

                  __magic_name__      				=    8
                  self.assertEqual(len(UpperCamelCase_ )				,    UpperCamelCase_ )  # TODO

                  # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                  # with the width and height being successively divided by 2, after every 2 blocks
                  for i in range(len(UpperCamelCase_ ) ):
                         self.assertEqual(
                             hidden_states[i].shape				,    torch.Size(
                                 [
                                     self.model_tester.batch_size,
                                     self.model_tester.embed_dims[i // 2],
                                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                 ] )				,    )

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __magic_name__      				=    True
                  check_hidden_states_output(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  __magic_name__      				=    True

                  check_hidden_states_output(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           def _config_zero_init(UpperCamelCase_ :	Optional[Any] ):
                  __magic_name__      				=    copy.deepcopy(UpperCamelCase_ )
                  for key in configs_no_init.__dict__.keys():
                         if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                                setattr(UpperCamelCase_				,    UpperCamelCase_				,    1e-1_0 )
                         if isinstance(getattr(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )				,    UpperCamelCase_ ):
                                __magic_name__      				=    _config_zero_init(getattr(UpperCamelCase_				,    UpperCamelCase_ ) )
                                setattr(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ )
                  return configs_no_init

           __magic_name__				,  __magic_name__      				=    self.model_tester.prepare_config_and_inputs_for_common()

           __magic_name__      				=    _config_zero_init(UpperCamelCase_ )
           for model_class in self.all_model_classes:
                  __magic_name__      				=    model_class(config=UpperCamelCase_ )
                  for name, param in model.named_parameters():
                         if param.requires_grad:
                                self.assertIn(
                                    ((param.data.mean() * 1e9) / 1e9).round().item()				,    [0.0, 1.0]				,    msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""				,    )







    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           pass


def 							A						(				)    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       return image






@require_torch
@require_vision
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs' ) if is_vision_available() else None







    @slow
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs' ).to(UpperCamelCase_ )

           __magic_name__      				=    self.default_image_processor
           __magic_name__      				=    prepare_img()
           __magic_name__      				=    image_processor(images=UpperCamelCase_				,    return_tensors='pt' ).to(UpperCamelCase_ )

           # forward pass
           with torch.no_grad():
                  __magic_name__      				=    model(**UpperCamelCase_ )

           # verify the logits
           __magic_name__      				=    torch.Size((1, 1_0_0_0) )
           self.assertEqual(outputs.logits.shape				,    UpperCamelCase_ )

           __magic_name__      				=    torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(UpperCamelCase_ )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]				,    UpperCamelCase_				,    atol=1e-4 ) )







"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case : Any     		=		logging.getLogger(__name__)
snake_case : Optional[Any]     		=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case : Optional[Any]     		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a_)}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}							,	)
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                  raise ValueError(
                      '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """The input training data file (a text file)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
    __UpperCAmelCase           = field(
        default=5							,	metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The number of processes to use for the preprocessing."""}							,	)
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }							,	)







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.train_file is not None:
                  __magic_name__      				=    self.train_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
           if self.validation_file is not None:
                  __magic_name__      				=    self.validation_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							A						(				__snake_case: Tuple ,     __snake_case: str  )    ->		Tuple:

       """simple docstring"""





       with open(__snake_case ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.loads(__snake_case  ) for line in f.read().splitlines() if (len(__snake_case  ) > 0 and not line.isspace())]
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    {c: dataset[c] for c in dataset.column_names}
       __magic_name__      				=    refs
       return Dataset.from_dict(__snake_case  )


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank  ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s' ,     __snake_case  )

       # Set seed before initializing model.
       set_seed(training_args.seed  )

       # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
       # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
       # (the dataset will be downloaded automatically from the datasets Hub).
       #
       # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
       # 'text' is found. You can easily tweak this behavior (see below).
       #
       # In distributed training, the load_dataset function guarantee that only one local process can concurrently
       # download the dataset.
       if data_args.dataset_name is not None:
              # Downloading and loading a dataset from the hub.
              __magic_name__      				=    load_dataset(data_args.dataset_name ,     data_args.dataset_config_name  )
              if "validation" not in datasets.keys():
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[:{data_args.validation_split_percentage}%]""" ,     )
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[{data_args.validation_split_percentage}%:]""" ,     )
       else:
              __magic_name__      				=    {}
              if data_args.train_file is not None:
                     __magic_name__      				=    data_args.train_file
              if data_args.validation_file is not None:
                     __magic_name__      				=    data_args.validation_file
              __magic_name__      				=    data_args.train_file.split('.'  )[-1]
              if extension == "txt":
                     __magic_name__      				=    'text'
              __magic_name__      				=    load_dataset(__snake_case ,     data_files=__snake_case  )
       # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
       # https://huggingface.co/docs/datasets/loading_datasets.html.

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.config_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    CONFIG_MAPPING[model_args.model_type]()
              logger.warning('You are instantiating a new config instance from scratch.'  )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""  )
                     config.update_from_string(model_args.config_overrides  )
                     logger.info(F"""New config: {config}"""  )

       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'use_fast': model_args.use_fast_tokenizer,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.tokenizer_name:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.tokenizer_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              raise ValueError(
                  'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
                  'You can do it from another script, save it, and load it from here, using --tokenizer_name.'  )

       if model_args.model_name_or_path:
              __magic_name__      				=    AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              logger.info('Training new model from scratch'  )
              __magic_name__      				=    AutoModelForMaskedLM.from_config(__snake_case  )

       model.resize_token_embeddings(len(__snake_case  )  )

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              __magic_name__      				=    datasets['train'].column_names
       else:
              __magic_name__      				=    datasets['validation'].column_names
       __magic_name__      				=    'text' if 'text' in column_names else column_names[0]

       __magic_name__      				=    'max_length' if data_args.pad_to_max_length else False

       def tokenize_function(__snake_case: str  ):
              # Remove empty lines
              __magic_name__      				=    [line for line in examples['text'] if len(__snake_case  ) > 0 and not line.isspace()]
              return tokenizer(examples['text'] ,     padding=__snake_case ,     truncation=__snake_case ,     max_length=data_args.max_seq_length  )

       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     num_proc=data_args.preprocessing_num_workers ,     remove_columns=[text_column_name] ,     load_from_cache_file=not data_args.overwrite_cache ,     )

       # Add the chinese references if provided
       if data_args.train_ref_file is not None:
              __magic_name__      				=    add_chinese_references(tokenized_datasets['train'] ,     data_args.train_ref_file  )
       if data_args.validation_ref_file is not None:
              __magic_name__      				=    add_chinese_references(
                  tokenized_datasets['validation'] ,     data_args.validation_ref_file  )
       # If we have ref files, need to avoid it removed by trainer
       __magic_name__      				=    data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              __magic_name__      				=    False

       # Data collator
       # This one will take care of randomly masking the tokens.
       __magic_name__      				=    DataCollatorForWholeWordMask(tokenizer=__snake_case ,     mlm_probability=data_args.mlm_probability  )

       # Initialize our Trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None ,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
                     __magic_name__      				=    model_args.model_name_or_path
              else:
                     __magic_name__      				=    None
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'train_results.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Train results *****'  )
                            for key, value in sorted(train_result.metrics.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir ,     'trainer_state.json'  )  )

    # Evaluation
       __magic_name__      				=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'  )

              __magic_name__      				=    trainer.evaluate()

              __magic_name__      				=    math.exp(eval_output['eval_loss']  )
              __magic_name__      				=    perplexity

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'eval_results_mlm_wwm.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Eval results *****'  )
                            for key, value in sorted(results.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

       return results


def 							A						(				__snake_case: List[str]  )    ->		Union[str, Any]:

       """simple docstring"""





       main()


if __name__ == "__main__":
 main()